mpw-007 / NWM N-Well*

the NWM shapes of all of mpw-007

40 of 40 dies rendered.

37,135,984 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 (28.2 MB in total, largest 1.4 MB), 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_NWM_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_NWM_reticle.png
the stitched reticle, full resolution
23.8 MB e915ab7f37483d78a46b81139c4986090407f4304fe31c5d3e836097c8188106c8e3a230 80a3ff90406d2456c66562c4ff230cc2d36d8eadaa35d11d1333c44a04f30349
mpw-007_NWM_reticle_overview.png
the same image scaled to fit 4096 px
2.1 MB d11f8c99e92bcad9533ef4baf7e1b337e3b3b80f3f7f6131e67ed23cd87a33b0939615aa d3c26d20240a5e0b06c68280684a51454e55620608086feabfddd0f7d0be08f6
mpw-007_NWM_tiles.json
where every tile belongs, and its checksum
13.5 kB 17588fc327b6e8d6a6550a1ca45b56d8752f05942febe522e2c8ac91f2c74c1c3c978c3a f4666fc6a55fcc47506b76c3fe7d901dc91403d88059fd02141a7750288267f7
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, 0 546.7 kB84c32aa21f3717e8cfefb76573fdb4ca5b97d79673f046c0bd83534d38c6fba7
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 667.4 kBfd51a41c5dd49a8669b78eb8ec515d6c2e39a14d687c26411559a45ecb35851b
A3 microwatt_mpw7 7576×1077615152, 0 858.5 kB85d84e081f8f6a344d250badfbad15764d0be1de4ebe8767fa764981b0dbed36
A4 yonga-can_controller 7576×1077622728, 0 482.5 kB3bf3add4ee51a8cbd9cbfe8d2eba6cd21f6061cee5f57f2fb13eb0bf848229db
A5 rocketalpha 7576×1077630304, 0 1.1 MB9d19146165b3521d7e2aefea713d1e5029b3d0f5303036d86404f78f68535d6e
A6 riscduino-dcore_d3_3 7576×1077637880, 0 1.0 MBbb3a5b8ec0fef9ba7681a4c12aa5caf41b422752e4bfbd809039daa967e50e94
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 1.2 MB9ecb91b6c5d177f7fb76736c766bf972189a4be5059ad07943517f97fc369583
B2 riscduino-score-s4-3 7576×107767576, 10776 1.1 MB7d2a6bd2d6012ae0eadabbf100a22d87ce88c39b9794cb6f482271970d6ad9ad
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 410.6 kBd23a4f301d29871f7285881876452597eee6ea5b07571a685b72ec7c322b41d6
B4 sramtestvehicleg3 7576×1077622728, 10776 442.5 kB6d5165879873f01300e41b036a8279af3421e0cff6024f0c41cd6e917b380971
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 458.9 kB925c9d13ec7713a54a6c2c2beca745b10ac261acbf1397a017c17c7a2f8995e9
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 531.6 kB6c9d63c00d2db5e37c6a6a9f8dd7d0047a055e9dbbf39e5ce223dac04498faf7
B7 reram_crossbar 7976×1077645456, 10776 486.0 kB965eeee7fd91d75e327797f1e830f972d8a7f1acf848e3f7c2568ff5dfc4e186
C1 trainable-nn 7576×107760, 21552 917.6 kB56c3b09cda3a25d740c81832976a8032803bf6320248bb5769c107a989645aa4
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 427.8 kBb5b7f0de287e10079c66d6b4b82351d17d5cf99953cbbc7e85adc7ef9af2d37e
C3 bitcoin_mining_asics 7576×1077615152, 21552 554.5 kBefa6c2fe98a4a140a44fd75c73565fd05e400cf96ab8c801e6aaad07af7acc40
C4 yonga-mcu 7576×1077622728, 21552 810.3 kBc6721646576ea12dd25b656bf62a4f41c2e5389f539d1a9daad31befdc516c06
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 1.1 MB92c74643576c4e1f4437b9e83af100a9b036b422c9c6672bf2a8c17ebf5b4d00
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 1.0 MB6b87baa216d9102770ae27ec7d2a7b03c4bd289189f44c02b11446252b82a3c1
D1 mixed_signal_circuits-jun13 7576×107760, 32328 478.1 kB179d79e15fddef1932025c79230542d7a1790bd144a109a9b95188565f522a45
D2 fpga_programming_management_unit 7576×107767576, 32328 678.9 kB3817ac97b62397eaa2931e495f3b61a7bf52fbba51abf19b5b0e8296616e84f2
D3 warp-v 7576×1077615152, 32328 511.8 kB1c7fd0d0ca8c17ee63b66465e2b3db77f64ad84854a738c56e0c4b4de5380ce6
D4 leaf__mpw7_ 7576×1077622728, 32328 505.3 kB8290701466b19d88a277b510d423e7176e2b3af3dac3048e739ebb33e4d48dcb
D5 in_memory_computing_sram 7576×1077630304, 32328 566.5 kB19d46aff148e4a622ddccd8cdca9fcd841e9808b3d49342cbbf0a3c9362c11e8
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 514.4 kBa2bca046f961c2563ece4c74d4201174f292cfc7d12ec0404dd412e7eb282777
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 435.4 kB482eed27aeef8dd07c0e1c6241a00592dfc948c665b73a32b7205ec4d7c717ce
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 525.6 kBc45c6b81d534decbbdbbfbc58f1463a7a8c14fa71906082163be76fc9a3db0ea
E2 in_memory_computing_rram 7576×107767576, 43104 548.2 kB94d67ebc16492189d54823c183240429246fa6a3b7665a5450dfa23bd0d46efb
E3 rram_imc_v2_flat 7576×1077615152, 43104 548.2 kB94d67ebc16492189d54823c183240429246fa6a3b7665a5450dfa23bd0d46efb
E4 topmetalse-dps 7576×1077622728, 43104 471.1 kB2e7fec944c040958b0b4311f9bc36e2f80c8c5728ea1ee2f7169a4dfff3b613c
E5 chaos_automaton 7576×1077630304, 43104 1.4 MB5a5ddd038320220b73de2dc76a48b9d23ef47c414a465ba6b442d20a17308ae1
E6 hehecore_resubmission 7576×1077637880, 43104 864.2 kBe40fcb678bf0aa9b6243d51999bae0dbf1ea3923d73a8acbfdbe225092cf0034
E7 waveform_generator 7976×1077645456, 43104 482.2 kB56440caf1be79eb358fd5ad9ad921dcf406da13ff02f88e0f8525936bb0a3351
F1 rift2core 7576×111760, 53880 567.9 kBd14fbec577727cea714d017f994f6f8a3ac9449a25e2c604770f93b1373c8ead
F2 rift2go_2300 7576×111767576, 53880 1.2 MBa30aa43b56f42d62c6719e8d8fe01be6832765799cdaf1bcc2c20a747f830994
F3 sloci_resubmission 7576×1117615152, 53880 447.7 kBd982e4eadc115a64c2c5fe876d026bb4086833da70c64cfde9b775db438fdaa4
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 494.0 kB91015c720725e13cfc9b0abd7f8691c995326e116e67a1e7ba7e7cdbe8dfd875
F5 crypto_aes128 7576×1117630304, 53880 650.7 kBe59dc6d9348c66e797ef63fbdd664a7507c6dfe44c0101fd82f7b53c930ce285
F6 graphics_controller 7576×1117637880, 53880 540.1 kBebd2d10da6a2a7e92fc6c95385c65cb51c9061261d19170f15e22b1e75e3d656
F7 prga-test 7976×1117645456, 53880 1.3 MB6f383e729b6ff7cd94b6be324a556799324a79d86b1b17305a98b9e065e86592

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 NWM on mpw-007

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

Mask layer
NWM — N-Well*
Process step
17 — N-well mask
Drawn layer
64:20
Layer on the mask-layer GDS
21:0
Detail
NWM = nwell sized (cnwm.3a outside hvi / 3b inside), with DEPMOS/pwell-resistor exemptions
Used in SKY130
yes

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.