mpw-008 / LVTNM Low Vt Nch*

the LVTNM shapes of all of mpw-008

40 of 40 dies rendered.

163,538 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 (7.3 MB in total, largest 238.7 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_LVTNM_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_LVTNM_reticle.png
the stitched reticle, full resolution
5.3 MB d631dd717cf51d0abae153a162406d73bfde324f2cf1bdf299b108f9a829e0e3927212c6 c5183c753d74b02fc507503280ae36e4fe3d2cedb0d214660db0aa36792a0398
mpw-008_LVTNM_reticle_overview.png
the same image scaled to fit 4096 px
427.5 kB fd55f4ec41bba631d47c18f055c933d69e7dd42365eb694a3fdf93f98410d6a449f2a63d 4b6a2169915cdae215053e1b39a255f4033c57af3fa930a1aa6f637349631925
mpw-008_LVTNM_tiles.json
where every tile belongs, and its checksum
13.5 kB aee81edfcd4933a511490ad464cbab66b4bfd515e1e2d8a0bdfdb654049397103196af6b 953739ae5c2cf80d33e919d9adc633c467c2f1bc4e9438800d204fca617910a8
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, 0 170.3 kB60b3e0c7b431b6dbd09cd1354b4ab981b855ae805cf4cc93bd592acfbc1604f5
A2 iit_indore_mm 7576×107767576, 0 165.7 kB2535039dc3d46f2529fa65251bdfc78db2fcb105997a3d40da4f62ba92fe4221
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 166.7 kBd6b3d5ff92819a82074dde4d1e34c8c0dd8378de9a808a64a93431119a2bfabf
A4 riscduino-dcore_d4_ 7576×1077622728, 0 199.2 kB313ad2672ad9a7b6253c178bfda9f505c683ac60b42a7182eed1843042fbe5af
A5 riscduino-qcore_q3_ 7576×1077630304, 0 206.1 kB925c87e8e9fec1feeae5843a68f5fd4267aef61ba957701273fee9522fa9807c
A6 caravel_mpw8 7576×1077637880, 0 166.7 kBd6b3d5ff92819a82074dde4d1e34c8c0dd8378de9a808a64a93431119a2bfabf
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 164.8 kB393bf5a14519553bf222778a092a7be13c73dedc99fe01468b77cd432ac7527e
B2 sramtestvehicleg4 7576×107767576, 10776 159.1 kBc86515aea253664138907268dc7f0704c51bb024eb6e288bad9b17d683c9557f
B3 reram_crossbar_rerun 7576×1077615152, 10776 168.1 kBd48760723def7471830ff4b2431457ac1276365ef942b705fc2328abea9f1849
B4 tinytapeout_skullart 7576×1077622728, 10776 238.7 kBa2ca93d6e843496b06981c9cf602532f2e4e690175c4ee6fcb953610c6a65078
B5 soomrv__mpw-8_ 7576×1077630304, 10776 190.1 kBf601b14b9998de26d3852740e30235726c945058de6bf5b48d44d375e0c987e9
B6 clock_divide_selector 7576×1077637880, 10776 164.5 kB11cf422cb9c203fa641f6bbd973b24f74413c6e30f536c7378154e0073f942c1
B7 reram_module_mpw8 7976×1077645456, 10776 162.7 kB1424ddd81dacdfbaabe9805e29aff9d2d6c767e72450013e9d6d3c81e3905dc3
C1 shapingthewaves_8 7576×107760, 21552 184.8 kB68c1f5a982935c83f1479e3a18233b2b3c0f2b89293a88cdb934ea4684102b16
C2 leosoc 7576×107767576, 21552 200.8 kBec8e9fb23dca895a833a91307321132c6a3606a29cae8028d7192afd9e42b426
C3 decrypt_aes128 7576×1077615152, 21552 169.7 kB650d83844729b0ab738c843de6b3854a9a3c55b05e5372ed51284afbae52966c
C4 approximate_adder_axha 7576×1077622728, 21552 166.7 kBd6b3d5ff92819a82074dde4d1e34c8c0dd8378de9a808a64a93431119a2bfabf
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 176.3 kBf6d3db28482ff79410fc8f9ce4cd3e3bbccaf8e93094a7e694c1939bd386fa44
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 201.0 kBb4148e77ab7738eee71bcf3fb601d7cc6d345ab33d09ff1bbe1e02d0c330d54d
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 175.5 kBb708b7b77c7287bddaf781560f1b9b060e4356b9598086395390dc0ece20104e
D2 hyperram_controller_for_sky130 7576×107767576, 32328 166.1 kB19f9271df81daf3a60fb07ae1b19e5110f9674187cd97133a40a743b7c0767cf
D3 hardened_alu 7576×1077615152, 32328 172.7 kB312586773dbb1987686e4956b6ab4710d7764b82c55cc648222308a074ebaa5d
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 200.2 kB45c230bef8ffe1d663586a32a3775a758c8b313895799a8dcc2ff8aae04fc86c
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 159.0 kB5030fe7b0ed99a84788adb9499267b63a251b83ee444cf7953b50fbdd446bfaa
D6 gf180_vs_sky130 7576×1077637880, 32328 173.3 kB72c54116d721f0e7bef4c93d91b8efe3b2cbfb343bb19d5aee1ce344c1c40033
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 170.2 kBdf9b42ea57452380166ca52b557b4c65540802c6d10f806fab5a431c74a1e9cb
E1 iiitb_sd_mpw8 7576×107760, 43104 166.7 kBd6b3d5ff92819a82074dde4d1e34c8c0dd8378de9a808a64a93431119a2bfabf
E2 iiitb_sfifo 7576×107767576, 43104 166.7 kBd6b3d5ff92819a82074dde4d1e34c8c0dd8378de9a808a64a93431119a2bfabf
E3 iiitb_alu 7576×1077615152, 43104 166.7 kBd6b3d5ff92819a82074dde4d1e34c8c0dd8378de9a808a64a93431119a2bfabf
E4 ppcpu2 7576×1077622728, 43104 187.7 kBd71d74def9a7b44057a79c15411a0adce23fd7fd661f8f22e62f4763314aaad9
E5 mega4_reram 7576×1077630304, 43104 220.7 kB7ca27052d968725f558dcff212878cbdf2b044b8c3a9076c43c896e98cc6e256
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 164.9 kB14b263d7c1053b7ec2754d32fbe02618728cc180a2ac5f0c172a2b9b49dc0271
E7 sky130cla 7976×1077645456, 43104 169.8 kBf0bc9f7fe33cd0c47b7b3d2906a5111ec5207207485e7cc6f28249a0edfdafcb
F1 uetrv_esoc_v2 7576×111760, 53880 181.6 kB6476d951dab5f6902ffe1ae6599ebb30c2065693a4170537b8d8bd1ca7359225
F2 sequencedetection1010 7576×111767576, 53880 170.5 kBe122aff8e12d91153c9512a73fc248c1892c1bd0d17192ce9cab6c629cade39d
F3 testasynctrimux-mpw8 7576×1117615152, 53880 168.7 kB8f5871d1ff98ae6ab7b2b2c2c239ae4059f7d97de5bef58163e2e82fc48f4069
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 169.6 kBc53692542f073bf79a2744db8f16057d460493edc2d6639a015924aaa1594b5d
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 170.5 kBe122aff8e12d91153c9512a73fc248c1892c1bd0d17192ce9cab6c629cade39d
F6 arbiter_puf 7576×1117637880, 53880 169.6 kBc53692542f073bf79a2744db8f16057d460493edc2d6639a015924aaa1594b5d
F7 unigate-sky 7976×1117645456, 53880 181.4 kB163c7657822d1c5cb58e975b9b6144262c4b4c5aa7d67956aa9bf31ca8fc1e9b

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 LVTNM on mpw-008

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

Mask layer
LVTNM — Low Vt Nch*
Process step
14 — Low Vt NOMOS mask
Drawn layer
125:44
Derived from drawn layers as
125:44 OR (64:20 AND (78:44 OR 81:2))
Layer on the mask-layer GDS
25:0
Detail
drawn lvtn plus a created part [nwell AND (hvtp OR areaid.ce)] OR (LV nwell over varactors); only the drawn part is rendered
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.