mpw-008 / NTM N-tip Implant

the NTM shapes of all of mpw-008

40 of 40 dies rendered.

129,184 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (7.7 MB in total, largest 247.2 kB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-008_NTM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-008_NTM_reticle.png
the stitched reticle, full resolution
5.5 MB cb5209686fffdf7c75f6ccf8d4a9a6b1f126b29c4d7b8a415cc5e1d7cc3981471faf186e af4386ca2f8a175b43a690e38c32f97d263e166478924c56e404cef5aaf776a1
mpw-008_NTM_reticle_overview.png
the same image scaled to fit 4096 px
588.3 kB e3c5a5522b8dab1f8b2a51fc3edc01d570d6b59a32c7ed150ec19bae34b22f5e01c1a792 e918c6f5638730433e19d3b951c42184c11b60085e1eee8d2dcc0ee4efcd3f90
mpw-008_NTM_tiles.json
where every tile belongs, and its checksum
13.4 kB 5a98358dcd1b9c0fc61ad4f1e8f64112e081889a8bfe1731d187174143e6443a23559594 1b1a23efde7e0aacfc5c26ecc94a85b162f61924ff1ca770fd237f8a4aced80e
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 wishbone_pio 7576×107760, 0 177.7 kB77ed51fdd3402572df305a7d8d5d388664256c09c92cb41e90beb7c9153871cb
A2 iit_indore_mm 7576×107767576, 0 173.2 kB48fdafad408bcbb694a3d4f00a7924094d0a8ec3713a1b16436b60f7ad2e0fe9
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 174.0 kB1fcfcf42d684ea74a1b351de0ff4468037b4da3f2e1834789241d004e47c8ca5
A4 riscduino-dcore_d4_ 7576×1077622728, 0 225.1 kB0d1846ff1fa19ca5ba0938cc98060b97821ad16dbf41f3ebfe1df7d310e12041
A5 riscduino-qcore_q3_ 7576×1077630304, 0 231.5 kBbebad39a86c001527aabf52740c8841e976cb9df2aed6838d8524d30bbd80248
A6 caravel_mpw8 7576×1077637880, 0 174.0 kB1fcfcf42d684ea74a1b351de0ff4468037b4da3f2e1834789241d004e47c8ca5
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 183.3 kB582d4ea02377cee79a2039aaa0867243ac20cf98c2c9b94ce7def0a3f7c4019a
B2 sramtestvehicleg4 7576×107767576, 10776 174.1 kB6809a95f6fbbd4e3c716592e8fea7c958b8ecfa4b80f48c7b2a2a05ee538f7c7
B3 reram_crossbar_rerun 7576×1077615152, 10776 213.0 kB49d577c17529e929e156fe74d587c2619118e8301e6353784de0b8a112b83d0b
B4 tinytapeout_skullart 7576×1077622728, 10776 247.2 kB53e33331ab04b8b02de7c8384d5a50a010655b8044d845076f975c93f5cfaa08
B5 soomrv__mpw-8_ 7576×1077630304, 10776 203.1 kBdbee1fba74f6257b1f289f5374c9fc56dee82b13004dbf6af87ba62a4d866225
B6 clock_divide_selector 7576×1077637880, 10776 171.8 kBc52662f830d85973954b3a95e8abc991785fe27ef66583bd50efa540782ce1af
B7 reram_module_mpw8 7976×1077645456, 10776 176.0 kBbed3a3f6ab6745883afd561e7bb06b1291d62ee92dc71f0103764d496f5561d8
C1 shapingthewaves_8 7576×107760, 21552 208.8 kB4c2e27eb621d0f124342908af6a45b4149cdab61517ef951aa10a4b79b91410e
C2 leosoc 7576×107767576, 21552 224.0 kBe63c7dca41d85e05a54bf136216b5d279a6dabead9b336d4917a3c4af57797f2
C3 decrypt_aes128 7576×1077615152, 21552 177.5 kBd724b36d4a89d11113eec1f7b75b27cdea7035d2eeeb6e44060225b2ffae8c16
C4 approximate_adder_axha 7576×1077622728, 21552 174.0 kB1fcfcf42d684ea74a1b351de0ff4468037b4da3f2e1834789241d004e47c8ca5
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 196.3 kBcfe2abeff24540377ad6d8a0964212c2947682c544d2c30c8c7bc77626aef7dc
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 172.3 kB1f4e201b3c025238960cec5faf5aed4777e592e7c58dffbcecbf8f46f351a35a
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 184.4 kBebf8490214ef74f5043cb29d5b91de649c84757b97dc454021322c6432a1bcdc
D2 hyperram_controller_for_sky130 7576×107767576, 32328 173.6 kB34680c6e136fb5404a6860c6f31f5238839eee8100f9dcd5d4fa6890b1d4fae8
D3 hardened_alu 7576×1077615152, 32328 181.2 kBab029eaf5a114073636850456d447214bbe8ac686cae631a7b7f00f0456ad040
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 227.7 kB750ccb2baea2882089b924a069576ec070b066a56c8fa5c08404aa79c9564652
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 166.1 kB339816751d851a97e821c7dee1696d5ade9b75c3a502e62ba64caf7e49d86001
D6 gf180_vs_sky130 7576×1077637880, 32328 181.6 kB0529c68770796a80575364326dce6b19be8ecb8dd0f3486acc34567f1f3e9006
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 178.0 kB89b566b01f076301fbe010dd9e2eef97f84de5f01c007ea22423b17faed7d40f
E1 iiitb_sd_mpw8 7576×107760, 43104 174.0 kB1fcfcf42d684ea74a1b351de0ff4468037b4da3f2e1834789241d004e47c8ca5
E2 iiitb_sfifo 7576×107767576, 43104 174.0 kB1fcfcf42d684ea74a1b351de0ff4468037b4da3f2e1834789241d004e47c8ca5
E3 iiitb_alu 7576×1077615152, 43104 174.0 kB1fcfcf42d684ea74a1b351de0ff4468037b4da3f2e1834789241d004e47c8ca5
E4 ppcpu2 7576×1077622728, 43104 194.2 kBb8387078d3af15cbc76b0e92cd19cb729862284418c4d7f86bdd72726dc7a0ab
E5 mega4_reram 7576×1077630304, 43104 174.8 kB4d40a2ec12843bd61ed99e8eb5a532a1acf54e218f761151759ad488f1c88074
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 172.1 kB1111acc917852ced6d6a22fbccba5679f819e85045d98352001120808c335942
E7 sky130cla 7976×1077645456, 43104 177.7 kBee7493f1d7b9351471504f1551d2aa7f8fcc0d7336478f487d8e02f2ddd7fbb7
F1 uetrv_esoc_v2 7576×111760, 53880 196.5 kB9f706e851c278f2a2cc2904208680f445a592d4bfd16f5c73eb1e5002887a679
F2 sequencedetection1010 7576×111767576, 53880 177.9 kBb76440807c5bf4f44bf467f28440d5d99aebe72702475743f361060ef91a37b3
F3 testasynctrimux-mpw8 7576×1117615152, 53880 176.1 kB5b64e18544614dc79190eed02044f1653f58657e879a4cfa00e68197b2242057
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 177.1 kB7b05a7709c3b9f4aba26273e6727ebae7cbc98f0fc111baa6a453dffd070a1c3
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 177.9 kBb76440807c5bf4f44bf467f28440d5d99aebe72702475743f361060ef91a37b3
F6 arbiter_puf 7576×1117637880, 53880 177.1 kB7b05a7709c3b9f4aba26273e6727ebae7cbc98f0fc111baa6a453dffd070a1c3
F7 unigate-sky 7976×1117645456, 53880 188.2 kB9de6516e1f21c69aa65039f07aba060179a87941bfb863957f92c8d56425833b

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About NTM on mpw-008

Resolved: reticle 5CS8017AC, run mpw-008, mask NTM

Mask layer
NTM — N-tip Implant
Process step
64 — NTM mask(tip fomation)
Drawn layers
64:20, 75:20, 11:44
Derived from drawn layers as
64:20 OR 11:44 OR (75:20 NOT 81:2)
Layer on the mask-layer GDS
27:0
Detail
created over nwell, ldntm and hvi (outside areaid.ce; rpm too in some flows) (cntm 26:20 is the fab's precomputed layer, not drawn)
Used in SKY130
yes

mpw-008 — SKY130 MPW-8

Reticle set
5CS8017AC
Run date
2022-11-19
Submissions closed
2022-12-31
Projects
144
Identification
inferred — Sequence after 8016=MPW-7 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-8 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-008/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.