mpw-007 / NCM N-Core Implant

the NCM shapes of all of mpw-007

40 of 40 dies rendered.

131,811 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 (7.0 MB in total, largest 250.3 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-007_NCM_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-007_NCM_reticle.png
the stitched reticle, full resolution
5.3 MB a35f62d8fc72e5679e31a217ae96780089ccb68ed0b954a75c3043c5b940be02f5007301 600e43d07ba24eada1140f21157f6c3774cc2fdcdaff165c92e2f39efc7fb749
mpw-007_NCM_reticle_overview.png
the same image scaled to fit 4096 px
506.3 kB 60ef53a68db29a94f415a461ca4198ac94d241d996701abc7ea4fe4f7009bc7715694f22 2a40e9599881d3f0755c3837bba43c17c905e2a4d3a1528c085e2ce739173ea2
mpw-007_NCM_tiles.json
where every tile belongs, and its checksum
13.5 kB 759a25cc2473895339b31df08537681d753ff26ec09d6cf37fb9549f1902b2e808af1684 b3b6919ca489cc81e23a1ae75b12eacd2254f48d2800296a399284ed656c6e74
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 tinytapeout 7576×107760, 0 186.0 kB334f953c92081425d4fbc70d5e4836ee1b7fc2017a351764b29227acb373d7dd
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 164.3 kBe612232eeb67056d67ebfe365e7794b5d8914739553af45488291de31183bdd7
A3 microwatt_mpw7 7576×1077615152, 0 184.3 kBbe559e19af1dc4cb00e878b18d51d12aa8242615b4f82eaaf669bc9de4172206
A4 yonga-can_controller 7576×1077622728, 0 149.7 kB5750a8d4b3a2fc36a7d556951e63c3bc79de47d808c28a901a474a6555d0f73a
A5 rocketalpha 7576×1077630304, 0 200.6 kBc71ed8e84138de23a165c1a9d18f2d55c9d24c6c41d4d03fe08ac790a22f193e
A6 riscduino-dcore_d3_3 7576×1077637880, 0 202.0 kBbf204a16fbea8eb606bdd70fac93449ab9458feb61b5c44ae88302b892db5604
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 207.4 kB74c3443355891ff9577a1b421f0fb9cc898b520893f8f865c1964b61a2a662ee
B2 riscduino-score-s4-3 7576×107767576, 10776 200.6 kBf6b2f62ad409a98c3185974b6128ce537059e0ba00c6d1bb79b7b3e88237e42d
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 144.7 kB7fade5e29db7799273fdfc6e190d9472be6c564e681d383c5c27f0b0f3e2cabf
B4 sramtestvehicleg3 7576×1077622728, 10776 153.1 kBea987683e1d8472b83bddbdc4d777d52c53a68995ea970023acbb6e5a2998afe
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 146.8 kB1f386eb443d5b604ab72d837f8d0a56628773d80b7d9878ad0b79f98f90e86cf
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 158.2 kB53e12010eac614543deeeb74b0c4fbe1d7f08f55568e5bd2c06259c9fb85bd6c
B7 reram_crossbar 7976×1077645456, 10776 166.8 kB54d476cae66b02ae17f7213ec8e295d9ed924e6b20be266283addd2b31bd8155
C1 trainable-nn 7576×107760, 21552 156.3 kB5eaf1439e54d2db11c0c70532a05f8ea437846c50ac1c68117592fa8b9bc677f
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 149.3 kBef65f795759d9b6206d1cf675da4b0d35d027eb243677be3d8df4a2faeea9508
C3 bitcoin_mining_asics 7576×1077615152, 21552 149.6 kB322ab5c99c017b8d89e758f9cfebe72691c283b903410c501abbf2c7f3e29ea2
C4 yonga-mcu 7576×1077622728, 21552 168.1 kBca11e3ebc210df29eb9ba2e31f9c16312ae70e7204c64488f0205496e38c7530
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 178.6 kB18ca3e9b52eb5086eb93e6b5d027e5f6f1eed0f63a5233741394d1640fa2c77d
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 213.7 kBa253dc9793a266848fc4ba5f7eb1049297032a459650ec6999a6442c86fa918f
D1 mixed_signal_circuits-jun13 7576×107760, 32328 144.7 kBe02c2ceac9ce14b8ec13f2cafef028dd3086803787cb32336fe17141056944aa
D2 fpga_programming_management_unit 7576×107767576, 32328 197.0 kBb91f9d6c9752b0b6db045ebd017590ddf70702fdd179d14450c731cd2bad3509
D3 warp-v 7576×1077615152, 32328 155.6 kBefb749a66a8a046b5611684def5796804e4971eb63bdcf140e9c380e71ae6fba
D4 leaf__mpw7_ 7576×1077622728, 32328 153.1 kBacae5164d4f1366ce6e5a94b1ee6644d6202fd342cf7a02c0fa8ee4d3b286e94
D5 in_memory_computing_sram 7576×1077630304, 32328 153.6 kB073c416220ee010d6703c17fde4cc21c00d73684565495446bd7e5b704aa1fac
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 150.1 kB2212bd0ab6ada2f9ceb312a65edb59881f20023ed839d06d90a719be28a2c44f
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 148.7 kBf2a67779bb73c468a710040c44bb8ca47fbb6c4a374644fa4d3df233625a711e
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 149.7 kB5750a8d4b3a2fc36a7d556951e63c3bc79de47d808c28a901a474a6555d0f73a
E2 in_memory_computing_rram 7576×107767576, 43104 156.2 kBc3b222d1f00bb0f56871d46d6132b660c359d87add7a0af49f32a9df3a54b032
E3 rram_imc_v2_flat 7576×1077615152, 43104 156.2 kBc3b222d1f00bb0f56871d46d6132b660c359d87add7a0af49f32a9df3a54b032
E4 topmetalse-dps 7576×1077622728, 43104 146.3 kB8bf912e4008c810a8bcff2a4ed4957f651f08aebbac8c70d1edeb58562d61b6d
E5 chaos_automaton 7576×1077630304, 43104 218.1 kB2d64dcb4e55e44838092f26dacc12bb1ae1cada461e96a68f1d4fe12f46d12a3
E6 hehecore_resubmission 7576×1077637880, 43104 195.9 kB23f0a93d2d4e9ba3ef6dd6c49507d2dcf90faff943a7aadaf08553707b0b83cd
E7 waveform_generator 7976×1077645456, 43104 159.7 kB01352ec890a49b054e48488302b2e58d686a03a29e6bead37af9a2089f491b7a
F1 rift2core 7576×111760, 53880 157.1 kB42c111ba340f440df0b8d19f846c353ce745e87e5ca6db52a49e31a932459ea7
F2 rift2go_2300 7576×111767576, 53880 163.3 kBdf238485b363167d47fb490659ab56386f5d7c2abbfe4443720b3e7bae90dd11
F3 sloci_resubmission 7576×1117615152, 53880 153.6 kBe3648b0ed72a744192f093cf5b1824f481c0293e93ece5ccf515710ae6813530
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 153.6 kB2996e1418366e61e4b95e5e506bf1455b1eb0ff7e9bcd8c266672936fa40fe14
F5 crypto_aes128 7576×1117630304, 53880 157.0 kB07eb17233c0d9b58f53e191817c81afd1b46ab4b90ba1a948a67cdcd605436e8
F6 graphics_controller 7576×1117637880, 53880 167.9 kB725283e4eaa7d47d205808efd08e87293f0befa21ce5c739e631c988c8ca302c
F7 prga-test 7976×1117645456, 53880 250.3 kBf82eeb4fdbcb2f745836951783e32b37c6e5650641e70703f5c63a7bd24d2f72

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 NCM on mpw-007

Resolved: reticle 5CS8016AC, run mpw-007, mask NCM

Mask layer
NCM — N-Core Implant
Drawn layer
92:44
Derived from drawn layers as
92:44 OR ((64:20 NOT 75:20) NOT 125:44)
Layer on the mask-layer GDS
17:0
Detail
drawn ncm in the core plus a created periphery part like HVTPM; only the drawn part is rendered
Used in SKY130
no

mpw-007 — SKY130 MPW-7

Reticle set
5CS8016AC
Run date
2022-07-08
Submissions closed
2022-09-12
Projects
106
Identification
inferred — Sequence after 5CS8014AC=MPW-6 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-7 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-007/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.